I. Field
The following description relates generally to image processing, and more particularly to a method and apparatus for region-based segmentation image processing using region mapping.
II. Background
Image processing includes techniques for analyzing image-based data to achieve some objective. The data may generally be represented by variations of intensity or color within a two-dimensional array. Important applications of image processing include object and feature detection. In these applications, some subset of image elements (pixels), taken together, may represent one or more features or physical objects within a scene or background captured by the image. Objects within an image may be detected by identifying a boundary or edge around the object.
Edge detection models have conventionally been used to determine the objects' boundaries. Edge detection is a technique for identifying areas in an image where the intensity changes sharply. Edges of an object or feature are presumed to exist in locations where steep intensity gradients are present. Edge techniques typically perform poorly, however, when the image characteristics do not provide a regular pattern of sharp contrasts between neighboring pixels such that edges of potential objects or features are not well defined. The absence of such patterns may occur, for example, when an object's edges are unclear or are blurred by image noise.
Alternative approaches to edge detection include region-based techniques that identify objects regardless of the presence or absence of clear edges. One such example includes the active contour without edges (ACWE) method. In these approaches, regions may be grouped according to one or more common image characteristics. Objects or features may be detected based on the use of the characteristics to calculate regions to which the image's pixels may be grouped. Unlike edge detection approaches, region-based techniques are not constrained to relying on image gradients to detect objects.
Unfortunately, current region-based approaches are computationally inefficient—and hence unduly time and resource intensive. In addition, many these approaches use pre-existing hard-coded techniques that fail to adapt to the images to be processed, rendering the approaches imprecise, and often producing unpredictable results.
Accordingly, a need in the art exists for systems and/or methodologies that achieve better performance for image processing, including processing for object detection.